Update README with LanceDB examples

README.md

@@ -3,6 +3,7 @@ license: cc-by-4.0
 task_categories:
 - object-detection
 - image-feature-extraction
+- lance
 language:
 - en
 tags:
@@ -18,24 +19,23 @@ size_categories:
 ---
 # COCO 2017 Object Detection (Lance Format)
 
-Lance-formatted version of the [COCO 2017 object detection benchmark](https://cocodataset.org/) — sourced from [`detection-datasets/coco`](https://huggingface.co/datasets/detection-datasets/coco) — with **123,287 images** and the full per-image list of bounding boxes, category labels, and CLIP image embeddings, all stored inline.
-
-## Why this version?
+A Lance-formatted version of the [COCO 2017 object detection benchmark](https://cocodataset.org/), sourced from [`detection-datasets/coco`](https://huggingface.co/datasets/detection-datasets/coco). Each row is one image with its inline JPEG bytes, the full per-image list of bounding boxes, COCO 80-class category ids and names, per-object areas, an OpenCLIP image embedding, and pre-built indices — all available directly from the Hub at `hf://datasets/lance-format/coco-detection-2017-lance/data`.
 
-Object detection datasets typically split images, annotations, and embeddings across multiple files (often three different formats: JPEG, JSON, NumPy). Lance keeps all of it in one tabular dataset:
+## Key features
 
-- one row per image,
-- the JPEG bytes, the bounding box list, the category labels, and the CLIP image embedding all live as columns on the same row,
-- `IVF_PQ` on the embedding column lets you do visual similarity search without leaving the dataset, and `LABEL_LIST` on `categories_present` lets you filter to "images containing a dog and a frisbee" in milliseconds.
+- **Inline JPEG bytes** in the `image` column — no sidecar files, no image folders.
+- **Per-object annotations as parallel list columns** — `bboxes`, `categories`, `category_names`, and `areas` are aligned position-for-position, so iterating boxes alongside their labels is a single row read.
+- **Pre-aggregated annotation summaries** — `num_objects` (int) and `categories_present` (deduped string list) precompute the predicates curation queries hit most.
+- **CLIP image embeddings** (`image_emb`, OpenCLIP ViT-B/32, 512-d, cosine-normalized) with a bundled `IVF_PQ` index for visual retrieval.
 
 ## Splits
 
 | Split | Rows |
 |-------|------|
 | `train.lance` | 117,000+ |
-| `val.lance`   | 4,950+ |
+| `val.lance`   | 4,950+   |
 
-(Counts come from the `detection-datasets/coco` redistribution; box counts: ~860k train / ~37k val.)
+Total annotated boxes: ~860k train / ~37k val.
 
 ## Schema
 
@@ -43,88 +43,77 @@ Object detection datasets typically split images, annotations, and embeddings ac
 |---|---|---|
 | `id` | `int64` | Row index within split |
 | `image` | `large_binary` | Inline JPEG bytes |
-| `image_id` | `int64` | COCO image id |
+| `image_id` | `int64` | COCO image id (natural join key) |
 | `width`, `height` | `int32` | Image dimensions in pixels |
-| `bboxes` | `list<list<float32, 4>>` | Each box is `[x_min, y_min, x_max, y_max]` in absolute pixel coords |
-| `categories` | `list<int32>` | COCO 80-class id (0-79) |
-| `category_names` | `list<string>` | Human-readable class name per object (e.g. `person`, `dog`, …) |
-| `areas` | `list<float32>` | Bounding-box area (pixels²) |
+| `bboxes` | `list<list<float32, 4>>` | Each box is `[x_min, y_min, x_max, y_max]` in absolute pixel coordinates |
+| `categories` | `list<int32>` | COCO 80-class id (0–79), aligned with `bboxes` |
+| `category_names` | `list<string>` | Human-readable class name per object (e.g. `person`, `dog`) |
+| `areas` | `list<float32>` | Bounding-box area in pixels², aligned with `bboxes` |
 | `num_objects` | `int32` | Number of annotated objects in the image |
-| `categories_present` | `list<string>` | Deduped class names — feeds the `LABEL_LIST` index for fast filtering |
-| `image_emb` | `fixed_size_list<float32, 512>` | OpenCLIP `ViT-B-32` image embedding (cosine-normalized) |
+| `categories_present` | `list<string>` | Deduped class names — feeds the `LABEL_LIST` index |
+| `image_emb` | `fixed_size_list<float32, 512>` | OpenCLIP ViT-B/32 image embedding (cosine-normalized) |
 
 ## Pre-built indices
 
-- `IVF_PQ` on `image_emb` — `metric=cosine`
-- `BTREE` on `image_id`, `num_objects`
-- `LABEL_LIST` on `categories_present` — supports `array_has_any` / `array_has_all` predicates
+- `IVF_PQ` on `image_emb` — vector similarity search (cosine)
+- `BTREE` on `image_id` — fast lookup by COCO image id
+- `BTREE` on `num_objects` — range filters on image complexity
+- `LABEL_LIST` on `categories_present` — supports `array_has_any` / `array_has_all` for class-presence filtering
+
+## Why Lance?
+
+1. **Blazing Fast Random Access**: Optimized for fetching scattered rows, making it ideal for random sampling, real-time ML serving, and interactive applications without performance degradation.
+2. **Native Multimodal Support**: Store text, embeddings, and other data types together in a single file. Large binary objects are loaded lazily, and vectors are optimized for fast similarity search.
+3. **Native Index Support**: Lance comes with fast, on-disk, scalable vector and FTS indexes that sit right alongside the dataset on the Hub, so you can share not only your data but also your embeddings and indexes without your users needing to recompute them.
+4. **Efficient Data Evolution**: Add new columns and backfill data without rewriting the entire dataset. This is perfect for evolving ML features, adding new embeddings, or introducing moderation tags over time.
+5. **Versatile Querying**: Supports combining vector similarity search, full-text search, and SQL-style filtering in a single query, accelerated by on-disk indexes.
+6. **Data Versioning**: Every mutation commits a new version; previous versions remain intact on disk. Tags pin a snapshot by name, so retrieval systems and training runs can reproduce against an exact slice of history.
+
+## Load with `datasets.load_dataset`
 
-## Quick start
+You can load Lance datasets via the standard HuggingFace `datasets` interface, suitable when your pipeline already speaks `Dataset` / `IterableDataset` or you want a quick streaming sample without installing anything Lance-specific.
 
 ```python
-import lance
+import datasets
 
-ds = lance.dataset("hf://datasets/lance-format/coco-detection-2017-lance/data/val.lance")
-print(ds.count_rows(), ds.schema.names, ds.list_indices())
+hf_ds = datasets.load_dataset("lance-format/coco-detection-2017-lance", split="val", streaming=True)
+for row in hf_ds.take(3):
+    print(row["image_id"], row["num_objects"], row["categories_present"][:5])
 ```
 
 ## Load with LanceDB
 
-These tables can also be consumed by [LanceDB](https://lancedb.github.io/lancedb/), the multimodal lakehouse and embedded search library built on top of Lance, for simplified vector search and other queries.
+LanceDB is the embedded retrieval library built on top of the Lance format ([docs](https://lancedb.com/docs)), and is the interface most users interact with. It wraps the dataset as a queryable table with search and filter builders, and is the entry point used by the Search, Curate, Evolve, Versioning, and Materialize-a-subset sections below.
 
 ```python
 import lancedb
 
 db = lancedb.connect("hf://datasets/lance-format/coco-detection-2017-lance/data")
 tbl = db.open_table("val")
-print(f"LanceDB table opened with {len(tbl)} images")
+print(len(tbl))
 ```
 
-> **Tip — for production use, download locally first.**
-> ```bash
-> hf download lance-format/coco-detection-2017-lance --repo-type dataset --local-dir ./coco-detection-2017-lance
-> ```
+## Load with Lance
 
-## Read one annotated image
+`pylance` is the Python binding for the Lance format and works directly with the format's lower-level APIs. Reach for it when you want to inspect dataset internals — schema, scanner, fragments, the list of pre-built indices.
 
 ```python
-import io
 import lance
-from PIL import Image, ImageDraw
 
 ds = lance.dataset("hf://datasets/lance-format/coco-detection-2017-lance/data/val.lance")
-row = ds.take([0], columns=["image", "bboxes", "category_names", "width", "height"]).to_pylist()[0]
-
-img = Image.open(io.BytesIO(row["image"])).convert("RGB")
-draw = ImageDraw.Draw(img)
-for (x1, y1, x2, y2), name in zip(row["bboxes"], row["category_names"]):
-    draw.rectangle([x1, y1, x2, y2], outline="red", width=3)
-    draw.text((x1 + 4, y1 + 4), name, fill="red")
-img.save("annotated.jpg")
+print(ds.count_rows(), ds.schema.names)
+print(ds.list_indices())
 ```
 
-## Filter by classes (LABEL_LIST index)
-
-```python
-import lance
-ds = lance.dataset("hf://datasets/lance-format/coco-detection-2017-lance/data/val.lance")
+> **Tip — for production use, download locally first.** Streaming from the Hub works for exploration, but heavy random access, ANN search, and any mutation are far faster against a local copy:
+> ```bash
+> hf download lance-format/coco-detection-2017-lance --repo-type dataset --local-dir ./coco-detection-2017-lance
+> ```
+> Then point Lance or LanceDB at `./coco-detection-2017-lance/data`.
 
-# Images that contain BOTH a person and a frisbee.
-rows = ds.scanner(
-    filter="array_has_all(categories_present, ['person', 'frisbee'])",
-    columns=["image_id", "category_names"],
-    limit=10,
-).to_table().to_pylist()
-
-# Images with at least 5 objects of any class.
-busy = ds.scanner(
-    filter="num_objects >= 5",
-    columns=["image_id", "num_objects"],
-    limit=10,
-).to_table().to_pylist()
-```
+## Search
 
-### Filter by classes with LanceDB
+The bundled `IVF_PQ` index on `image_emb` makes approximate-nearest-neighbor visual retrieval a single call. In production you would encode a query image through the same OpenCLIP ViT-B/32 model used at ingest and pass the resulting 512-d vector to `tbl.search(...)`. The example below uses the embedding stored on row 42 as a runnable stand-in, so the snippet works without loading any model.
 
 ```python
 import lancedb
@@ -132,41 +121,117 @@ import lancedb
 db = lancedb.connect("hf://datasets/lance-format/coco-detection-2017-lance/data")
 tbl = db.open_table("val")
 
-rows = (
+seed = (
     tbl.search()
-    .where("array_has_all(categories_present, ['person', 'frisbee'])")
-    .select(["image_id", "category_names"])
+    .select(["image_emb", "image_id", "categories_present"])
+    .limit(1)
+    .offset(42)
+    .to_list()[0]
+)
+
+hits = (
+    tbl.search(seed["image_emb"])
+    .metric("cosine")
+    .select(["image_id", "categories_present", "num_objects"])
     .limit(10)
     .to_list()
 )
+print("query categories:", seed["categories_present"])
+for r in hits:
+    print(f"  image_id={r['image_id']:>10}  n={r['num_objects']:>3}  cats={r['categories_present'][:5]}")
+```
+
+Because the embeddings are cosine-normalized, the first hit will typically be the source image itself — a useful sanity check. Tune `nprobes` and `refine_factor` to trade recall against latency for your workload.
+
+## Curate
+
+Curation for a detection workflow usually means picking images that contain a specific class combination, possibly bounded by scene complexity. The `LABEL_LIST` index on `categories_present` makes class-presence predicates trivial, and Lance evaluates them inside the same scan as range filters on `num_objects` or `width`/`height`. The bounded `.limit(500)` keeps the result small and inspectable, and the `image` column is left out of the projection so the candidate scan is dominated by annotation metadata, not JPEG bytes.
+
+```python
+import lancedb
+
+db = lancedb.connect("hf://datasets/lance-format/coco-detection-2017-lance/data")
+tbl = db.open_table("val")
 
-busy = (
+candidates = (
     tbl.search()
-    .where("num_objects >= 5")
-    .select(["image_id", "num_objects"])
-    .limit(10)
+    .where(
+        "array_has_all(categories_present, ['person', 'frisbee']) "
+        "AND num_objects BETWEEN 3 AND 12",
+        prefilter=True,
+    )
+    .select(["image_id", "categories_present", "num_objects", "width", "height"])
+    .limit(500)
     .to_list()
 )
+print(f"{len(candidates)} candidates; first image_id: {candidates[0]['image_id']}")
 ```
 
-## Visual similarity search
+The result is a plain list of dictionaries, ready to inspect, persist as a manifest of `image_id`s, or feed into the Evolve and Train workflows below. Swapping `array_has_all` for `array_has_any` widens recall to images containing any of the listed classes; replacing the structural predicate with `num_objects >= 10` selects busy scenes for crowd-detection ablations.
+
+## Evolve
+
+Lance stores each column independently, so a new column can be appended without rewriting the existing data. The lightest form is a SQL expression: derive the new column from columns that already exist, and Lance computes it once and persists it. The example below adds a `has_person` flag, an `aspect_ratio`, and a `max_box_area` that surfaces the largest annotated object area per image — all of which can then be used directly in `where` clauses without recomputing the predicate on every query.
+
+> **Note:** Mutations require a local copy of the dataset, since the Hub mount is read-only. See the Materialize-a-subset section at the end of this card for a streaming pattern that downloads only the rows and columns you need, or use `hf download` to pull the full split first.
+
+```python
+import lancedb
+
+db = lancedb.connect("./coco-detection-2017-lance/data")  # local copy required for writes
+tbl = db.open_table("val")
+
+tbl.add_columns({
+    "has_person": "array_has_any(categories_present, ['person'])",
+    "aspect_ratio": "CAST(width AS DOUBLE) / CAST(height AS DOUBLE)",
+    "max_box_area": "array_max(areas)",
+    "crowded": "num_objects >= 10",
+})
+```
+
+If the values you want to attach already live in another table (offline predictions from a baseline detector, per-image difficulty scores, or a second-pass embedding), merge them in by joining on `image_id`:
 
 ```python
-import lance
 import pyarrow as pa
 
-ds = lance.dataset("hf://datasets/lance-format/coco-detection-2017-lance/data/val.lance")
-emb_field = ds.schema.field("image_emb")
-ref = ds.take([0], columns=["image_emb"]).to_pylist()[0]["image_emb"]
-query = pa.array([ref], type=emb_field.type)
-
-neighbors = ds.scanner(
-    nearest={"column": "image_emb", "q": query[0], "k": 5},
-    columns=["image_id", "category_names"],
-).to_table().to_pylist()
+predictions = pa.table({
+    "image_id": pa.array([397133, 37777, 252219], type=pa.int64()),
+    "baseline_map": pa.array([0.31, 0.48, 0.22]),
+})
+tbl.merge(predictions, on="image_id")
+```
+
+The original columns and indices are untouched, so existing code that does not reference the new columns continues to work unchanged. New columns become visible to every reader as soon as the operation commits. For column values that require a Python computation (e.g., running a second detector over the image bytes), Lance provides a batch-UDF API — see the [Lance data evolution docs](https://lance.org/guide/data_evolution/).
+
+## Train
+
+Projection lets a training loop read only the columns each step actually needs. LanceDB tables expose this through `Permutation.identity(tbl).select_columns([...])`, which plugs straight into the standard `torch.utils.data.DataLoader` so prefetching, shuffling, and batching behave as in any PyTorch pipeline. For a detector training run, project the JPEG bytes alongside the parallel annotation columns the loss consumes — boxes, category ids, and (optionally) areas. Columns added in the Evolve section above cost nothing per batch until they are explicitly projected.
+
+```python
+import lancedb
+from lancedb.permutation import Permutation
+from torch.utils.data import DataLoader
+
+db = lancedb.connect("hf://datasets/lance-format/coco-detection-2017-lance/data")
+tbl = db.open_table("train")
+
+train_ds = Permutation.identity(tbl).select_columns(
+    ["image", "bboxes", "categories", "areas"]
+)
+loader = DataLoader(train_ds, batch_size=16, shuffle=True, num_workers=4,
+                    collate_fn=lambda b: b)  # detection targets are ragged
+
+for batch in loader:
+    # batch is a list of dicts: decode each JPEG, stack the bboxes / categories
+    # into the target dictionary your detector expects, forward, loss...
+    ...
 ```
 
-### LanceDB visual similarity search
+Switching feature sets is a configuration change: passing `["image_emb", "categories_present"]` to `select_columns(...)` on the next run skips JPEG decoding entirely and reads only the cached 512-d vectors plus the deduped class list, which is the right shape for training a lightweight multi-label classifier or a class-presence probe on top of frozen features.
+
+## Versioning
+
+Every mutation to a Lance dataset, whether it adds a column, merges labels, or builds an index, commits a new version. Previous versions remain intact on disk. You can list versions and inspect the history directly from the Hub copy; creating new tags requires a local copy since tags are writes.
 
 ```python
 import lancedb
@@ -174,23 +239,51 @@ import lancedb
 db = lancedb.connect("hf://datasets/lance-format/coco-detection-2017-lance/data")
 tbl = db.open_table("val")
 
-ref = tbl.search().limit(1).select(["image_emb"]).to_list()[0]
-query_embedding = ref["image_emb"]
+print("Current version:", tbl.version)
+print("History:", tbl.list_versions())
+print("Tags:", tbl.tags.list())
+```
+
+Once you have a local copy, tag a version for reproducibility:
 
-results = (
-    tbl.search(query_embedding)
-    .metric("cosine")
-    .select(["image_id", "category_names"])
-    .limit(5)
-    .to_list()
-)
+```python
+local_db = lancedb.connect("./coco-detection-2017-lance/data")
+local_tbl = local_db.open_table("val")
+local_tbl.tags.create("detector-baseline-v1", local_tbl.version)
 ```
 
-## Why Lance?
+A tagged version can be opened by name, or any version reopened by its number, against either the Hub copy or a local one:
+
+```python
+tbl_v1 = db.open_table("val", version="detector-baseline-v1")
+tbl_v5 = db.open_table("val", version=5)
+```
+
+Pinning supports two workflows. An evaluation harness locked to `detector-baseline-v1` keeps scoring against the exact same boxes and category ids while the dataset evolves in parallel; newly merged predictions or evolved columns do not change what the tag resolves to. A training experiment pinned to the same tag can be rerun later against the exact same images and annotations, so changes in mAP reflect model changes rather than data drift. Neither workflow needs shadow copies or external manifest tracking.
+
+## Materialize a subset
+
+Reads from the Hub are lazy, so exploratory queries only transfer the columns and row groups they touch. Mutating operations (Evolve, tag creation) need a writable backing store, and a training loop benefits from a local copy with fast random access. Both can be served by a subset of the dataset rather than the full split. The pattern is to stream a filtered query through `.to_batches()` into a new local table; only the projected columns and matching row groups cross the wire, and the bytes never fully materialize in Python memory.
+
+```python
+import lancedb
+
+remote_db = lancedb.connect("hf://datasets/lance-format/coco-detection-2017-lance/data")
+remote_tbl = remote_db.open_table("train")
+
+batches = (
+    remote_tbl.search()
+    .where("array_has_any(categories_present, ['dog', 'cat']) AND num_objects >= 2")
+    .select(["image_id", "image", "bboxes", "categories", "category_names",
+             "areas", "num_objects", "categories_present", "image_emb"])
+    .to_batches()
+)
+
+local_db = lancedb.connect("./coco-pets-subset")
+local_db.create_table("train", batches)
+```
 
-- One dataset carries images + boxes + categories + areas + embeddings + indices — no JSON sidecars.
-- On-disk vector and label-list indices live next to the data, so filters and ANN search work on local copies and on the Hub.
-- Schema evolution: add columns (segmentation polygons, keypoints, panoptic ids, fresh embeddings) without rewriting the data.
+The resulting `./coco-pets-subset` is a first-class LanceDB database. Every snippet in the Evolve, Train, and Versioning sections above works against it by swapping `hf://datasets/lance-format/coco-detection-2017-lance/data` for `./coco-pets-subset`.
 
 ## Source & license